RDKit Training: Learn Cheminformatics & Molecular Modeling for Drug Discovery

Master RDKit for cheminformatics and drug discovery! Learn molecular modeling, QSAR, fingerprinting, and AI-driven analysis with hands-on training.

Module 1: Introduction to RDKit and Cheminformatics

1.1 Overview of RDKit

• What is RDKit? Importance in cheminformatics and drug discovery
• Key features and capabilities
• Comparison with other cheminformatics tools

1.2 Installation and Setup

• Installing RDKit on Windows, Linux, and macOS
• Setting up RDKit with Python
• Integrating RDKit with Jupyter Notebook

1.3 Understanding Chemical Data Formats

• Introduction to molecular representations
• SMILES, InChI, MOL, and SDF file formats
• Conversion between different chemical formats

1.4 First Steps with RDKit

• Loading and visualizing molecules
• Basic molecular operations (atom and bond manipulations)
• Generating 2D molecular structures

1.5 Hands-on Exercises

• Write Python scripts to load and visualize molecular structures
• Convert SMILES strings into different formats
• Basic molecular manipulation exercises

Module 2: Molecular Representations and Structure Handling in RDKit

2.1 Understanding Molecular Objects in RDKit

• Defining molecules as RDKit objects using Chem.MolFromSmiles () and Chem.MolFromMolFile ()
• Accessing molecular properties: Atoms, Bonds, and Rings
• Printing molecular information using MolToMolBlock () and MolToSmiles ()
• Identifying explicit and implicit hydrogens in molecular structures

2.2 Working with Different Molecular Representations

• Understanding molecular representations:
  • SMILES: Simplified Molecular Input Line Entry System
  • InChI: International Chemical Identifier
  • MOL & SDF: Structural Data File formats
• Reading and parsing chemical structures:
  • Converting between SMILES, InChI, and MOL using Chem.MolToSmiles () , Chem.MolToInchi ()
  • Using Chem.AddHs () and Chem.RemoveHs () for hydrogen handling
  • Checking molecular validity using rdkit.Chem.rdmolops.SanitizeMol ()

2.3 Loading and Manipulating Molecular Structures

• Reading molecular structures from:
  • SMILES strings: mol = Chem.MolFromSmiles ("CCO")
  • MOL files: mol = Chem.MolFromMolFile ("molecule.mol")
  • SDF files: Using SDMolSupplier to iterate through multi-molecule files
• Modifying molecular structures:
  • Accessing atoms and bonds: mol.GetAtoms () and mol.GetBonds ()
  • Changing atomic properties (valency, hybridization)
  • Creating substructures and fragments using rdkit.Chem.FragmentOnBonds ()

2.4 Generating and Optimizing 3D Molecular Structures

• Embedding molecules in 3D space:
  • Using rdkit.Chem.AllChem.EmbedMolecule () for 3D coordinates
  • Energy minimization using rdkit.Chem.AllChem.UFFOptimizeMolecule ()
  • Generating multiple conformations with rdkit.Chem.AllChem.EmbedMultipleConfs ()
• Analyzing 3D molecular structures:
  • Measuring bond lengths and angles
  • Comparing RMSD between conformers

2.5 Saving and Exporting Molecular Structures

• Exporting molecules to:
  • SMILES format: smiles = Chem.MolToSmiles (mol)
  • MOL file: Chem.MolToMolFile (mol, "output.mol")
  • SDF file: Writing multiple molecules using SDWriter
• Rendering molecules as images:
  • Using rdkit.Chem.Draw.MolToImage () for static visualization
  • Generating 2D depictions with rdkit.Chem.Draw.MolDraw2D

2.6 Hands-on Exercises

• Convert a list of SMILES strings to MOL format and save them
• Generate 3D structures for a set of molecules and optimize their energy
• Extract substructures from a given set of molecular structures
• Compare different representations of a molecule and analyze their differences
• Write a Python script to iterate through an SDF file and retrieve molecular properties

Module 3: Molecular Fingerprints, Descriptors, and Similarity Searching in RDKit

3.1 Introduction to Molecular Fingerprints

• Understanding molecular fingerprints and their significance
• Types of fingerprints in RDKit:
  • Morgan (ECFP) – Extended Connectivity Fingerprint
  • MACCS – Molecular ACCess System keys
  • Atom-Pair & Topological Torsion – Path-based fingerprints
  • RDKit Fingerprint – Default path-based fingerprint

3.2 Generating and Analyzing Molecular Fingerprints

• Creating fingerprints:
  • Generating Morgan fingerprints: rdkit.Chem.AllChem.GetMorganFingerprintAsBitVect (mol, radius=2)
  • Generating MACCS keys: rdkit.Chem.rdMolDescriptors.GetMACCSKeysFingerprint (mol)
  • Generating RDKit standard fingerprints: rdkit.Chem.rdFingerprintGenerator.GetRDKitFPGenerator ()
• Understanding bit vectors and feature encoding
• Comparing different fingerprint methods for molecular similarity

3.3 Molecular Descriptor Calculation

• What are molecular descriptors? Importance in QSAR and cheminformatics
• Calculating physicochemical descriptors:
  • Molecular weight: rdkit.Chem.Descriptors.MolWt (mol)
  • LogP (lipophilicity) : rdkit.Chem.Crippen.MolLogP (mol)
  • Topological polar surface area (TPSA) : rdkit.Chem.rdMolDescriptors.CalcTPSA (mol)
  • Hydrogen bond donors and acceptors: rdkit.Chem.rdMolDescriptors.CalcNumHBD (mol) and CalcNumHBA (mol)
• Extracting multiple descriptors using rdkit.Chem.Descriptors module
• Storing descriptor data in Pandas DataFrames for analysis

3.4 Molecular Similarity Search

• Understanding similarity metrics:
  • Tanimoto similarity: DataStructs.FingerprintSimilarity (fp1, fp2)
  • Dice, Cosine, and Sokal similarity measures
• Performing similarity searches in large molecular databases
• Building a molecule similarity search function using RDKit
• Visualizing similar molecules using Matplotlib and RDKit drawing tools

3.5 Hands-on Exercises

• Generate and compare fingerprints for a set of molecules
• Calculate molecular descriptors for a dataset and analyze trends
• Perform a Tanimoto similarity search using a reference molecule
• Develop a Python script that ranks compounds by similarity to a given drug molecule

Module 4: Substructure Searching and Functional Group Identification in RDKit

4.1 Introduction to Substructure Searching

• Understanding molecular substructures and pattern matching
• Difference between exact structure matching and substructure matching
• Importance of SMARTS (SMiles ARbitrary Target Specification) notation

4.2 Performing Substructure Searches

• Using RDKit to identify substructures:
  • Checking if a molecule contains a substructure: mol.HasSubstructMatch (submol)
  • Finding multiple matches within a molecule: mol.GetSubstructMatches (submol)
  • Highlighting matched substructures in molecular visualization
• Creating SMARTS queries for complex molecular patterns
• Filtering large molecular datasets based on substructures

4.3 Identifying Functional Groups

• Understanding functional groups and their chemical properties
• Using RDKit to detect common functional groups:
  • Carboxyl (-COOH) , Hydroxyl (-OH) , Amino (-NH2) , Ketones (C=O) , etc.
  • Using rdkit.Chem.Fragments module for functional group identification
  • SMARTS patterns for detecting custom functional groups
• Counting occurrences of functional groups in a molecule
• Generating molecular fingerprints based on functional group presence

4.4 Filtering and Screening Molecules

• Creating molecular filters based on substructure presence
• Filtering databases based on:
  • Presence of reactive functional groups
  • Drug-likeness rules (Lipinski’s Rule of 5)
  • Structural complexity
• Automating substructure-based molecule selection using Pandas and RDKit

4.5 Hands-on Exercises

• Perform substructure searches in a molecular dataset
• Write a script to detect and count specific functional groups in molecules
• Filter a list of molecules based on drug-likeness criteria
• Visualize and highlight substructures in chemical compounds

Module 5: Chemical Reactions, Molecular Transformations, and Scaffold Analysis in RDKit

5.1 Introduction to Chemical Reactions in RDKit

• Understanding reaction representation in cheminformatics
• Defining chemical reactions using SMARTS patterns
• Basic reaction operations using rdkit.Chem.rdChemReactions

5.2 Defining and Applying Chemical Reactions

• Creating reaction templates using SMARTS:
  • Defining a reaction: rdkit.Chem.rdChemReactions.ReactionFromSmarts ()
  • Identifying reactants, reagents, and products
  • Handling reaction specificity and stereochemistry
• Applying reactions to molecules:
  • Single-step transformations
  • Multi-step synthetic reaction planning

5.3 Scaffold Analysis and Molecular Core Extraction

• Understanding molecular scaffolds and their importance
• Extracting molecular cores using the Murcko Scaffold algorithm
• Analyzing core structures in a dataset of compounds

5.4 Functional Group Transformations

• Defining transformation rules using SMARTS
• Performing in-silico derivatization and retrosynthesis
• Automating functional group modifications for drug design

5.5 Hands-on Exercises

• Define and apply a SMARTS-based reaction to a dataset
• Extract Murcko scaffolds from a list of drug-like molecules
• Perform functional group modifications on a given molecule set
• Simulate multi-step reaction pathways

Module 6: Machine Learning with RDKit – QSAR Modeling and Predictive Analytics

6.1 Introduction to QSAR (Quantitative Structure-Activity Relationship)

• Understanding the concept of QSAR in cheminformatics
• Applications of QSAR modeling in drug discovery and toxicology
• Workflow for building a QSAR model

6.2 Extracting Molecular Features for Machine Learning

• Generating molecular descriptors using RDKit:
  • Physicochemical properties (LogP, MW, TPSA, etc.)
  • Structural features (hydrogen bond donors/acceptors, rotatable bonds)
  • Topological and 3D descriptors
• Generating molecular fingerprints as input features:
  • Morgan fingerprints (ECFP)
  • MACCS keys
  • RDKit fingerprint
• Converting molecular data into numerical format using Pandas and NumPy

6.3 Data Preprocessing for QSAR Modeling

• Handling missing and imbalanced data
• Feature selection techniques:
  • Correlation-based filtering
  • Principal Component Analysis (PCA)
• Scaling and normalization of molecular descriptors

6.4 Building Machine Learning Models for Molecular Property Prediction

• Introduction to machine learning algorithms for QSAR:
  • Linear Regression
  • Random Forest
  • Support Vector Machines (SVM)
  • Neural Networks
• Training a predictive model using Scikit-learn
• Cross-validation and performance evaluation (R², RMSE, MAE)

6.5 Molecular Property Prediction Using Pretrained Models

• Building predictive models for:
  • Drug-likeness
  • Toxicity prediction
  • Bioavailability screening
• Using machine learning for virtual screening of compound libraries

6.6 Hands-on Exercises

• Extract molecular descriptors and fingerprints from a dataset
• Preprocess data and perform feature selection
• Train a QSAR model to predict bioactivity
• Evaluate model performance and optimize hyperparameters
• Use the trained model to screen a virtual compound library

Module 7: Virtual Screening, Molecular Docking, and AI-Driven Drug Discovery with RDKit

7.1 Introduction to Virtual Screening

• What is virtual screening? Importance in drug discovery
• Ligand-based vs. structure-based virtual screening
• Workflow for in-silico screening using RDKit

7.2 Molecular Library Preparation for Screening

• Curating and preparing chemical libraries
• Filtering molecules using Lipinski’s Rule of 5
• Identifying and removing PAINS (Pan Assay Interference Compounds)
• Generating multiple conformations for screening

7.3 Similarity-Based Virtual Screening

• Using molecular fingerprints for virtual screening
• Measuring similarity using Tanimoto, Dice, and Cosine metrics
• Ranking molecules based on similarity to a reference compound

7.4 Structure-Based Molecular Docking

• Introduction to molecular docking and scoring functions
• Generating 3D molecular conformers for docking
• Docking ligands into target proteins using RDKit and external docking tools
• Analyzing binding interactions and docking scores

7.5 AI-Driven Drug Discovery with RDKit

• Using deep learning models for molecular property prediction
• Generating de novo drug-like molecules using generative models
• Integrating RDKit with TensorFlow and PyTorch for AI-based drug design

7.6 Hands-on Exercises

• Perform virtual screening on a library of drug-like molecules
• Use RDKit to filter and optimize hit compounds
• Dock selected molecules into a protein binding site
• Analyze docking results and refine ligand structures
• Train a deep learning model to generate novel drug candidates

Module 8: RDKit Integration with Data Science, Visualization, and Workflow Automation

8.1 RDKit and Data Science: Handling Large-Scale Molecular Datasets

• Reading and processing large datasets (SDF, CSV, and JSON)
• Using Pandas with RDKit for molecular data handling
• Optimizing performance for large-scale molecular processing

8.2 Advanced Molecular Visualization Techniques

• Generating high-quality molecular images using rdkit.Chem.Draw
• Customizing molecular drawings with atom and bond highlights
• Rendering 3D molecular structures using Py3Dmol and RDKit

8.3 Automating Workflow Pipelines with RDKit

• Building automated molecular filtering and processing workflows
• Batch processing and parallel computation techniques
• Using RDKit with Jupyter Notebooks for interactive analysis

8.4 RDKit Integration with External Tools and Libraries

• Connecting RDKit with Open Babel for format conversion
• Integrating RDKit with PyMOL for advanced visualization
• Using RDKit with deep learning libraries (TensorFlow, PyTorch)

8.5 Deploying RDKit-Based Applications

• Creating web-based cheminformatics applications using RDKit and Flask
• Building API services for molecular processing
• Deploying RDKit pipelines on cloud platforms (AWS, Google Cloud, Azure)

8.6 Hands-on Exercises

• Analyze and visualize a large molecular dataset using RDKit and Pandas
• Automate a workflow for filtering and saving drug-like molecules
• Integrate RDKit with Open Babel for batch molecular conversions
• Deploy a simple RDKit-based cheminformatics API

Module 9: Customizing RDKit for Advanced Applications and Research

9.1 Extending RDKit Functionality with Custom Scripts

• Modifying RDKit source code for specific research needs
• Creating custom molecular descriptors using Python
• Developing new chemical reaction rules with SMARTS patterns

9.2 Custom Fingerprint Design and Optimization

• Understanding the limitations of existing fingerprints
• Defining custom molecular fingerprint algorithms
• Optimizing fingerprints for improved QSAR performance

9.3 Machine Learning Model Deployment with RDKit

• Building and deploying trained QSAR models
• Creating web-based molecular screening tools
• Using cloud-based ML pipelines for large-scale cheminformatics

9.4 Implementing Custom Molecular Property Calculations

• Writing Python functions for unique molecular descriptors
• Automating molecular data processing for research applications
• Benchmarking custom descriptors against standard RDKit features

9.5 Integrating RDKit with High-Performance Computing (HPC)

• Running RDKit on GPU-accelerated environments
• Parallelizing cheminformatics tasks for large datasets
• Optimizing RDKit workflows for computational efficiency

9.6 Hands-on Exercises

• Develop and test a custom fingerprint algorithm
• Build a web tool for virtual screening using RDKit and Flask
• Deploy a cloud-based cheminformatics pipeline using RDKit
• Optimize an RDKit workflow for parallel execution on HPC clusters

Module 10: Real-World Case Studies and Advanced Project Work

10.1 Case Study: Drug Repurposing with RDKit

• Understanding the principles of drug repurposing
• Screening existing drugs for new targets using molecular similarity
• Analyzing structural and functional properties for repurposing potential

10.2 Case Study: Toxicity Prediction Using QSAR Models

• Building a predictive model for chemical toxicity
• Using molecular descriptors and machine learning for hazard assessment
• Validating and interpreting toxicity predictions

10.3 Case Study: Screening Natural Compounds for Drug Discovery

• Analyzing a dataset of plant-derived molecules
• Predicting bioavailability and ADMET properties
• Identifying lead compounds using docking and similarity screening

10.4 Case Study: Custom Molecular Design for a Target Protein

• Defining molecular requirements based on target structure
• Generating novel compounds using RDKit and AI-driven molecular design
• Docking simulations and optimization of lead compounds

10.5 Advanced Project Work

• Developing a cheminformatics pipeline for high-throughput screening
• Building an AI-based drug discovery model integrating RDKit and deep learning
• Creating a web-based molecular database with search and filtering features
• Automating molecular synthesis planning with RDKit and SMARTS-based reactions

10.6 Final Assessment and Certification

• Comprehensive project review and evaluation
• Submission of an independent research project using RDKit
• Certification of completion based on performance and practical assignments

RDKit Training Pricing (Given in Dollars keeping world wide queries into consideration)

Note: Only 30 mins to 60 mins class per day is entertained by our team, Tuesday to Friday are working days, Saturday, Sunday and Monday Class seekers will be charged 30% extra on total fee. All public holidays are as per Indian Calendar.

18% GST is charged extra to the selected slot fee.

1. Basic RDKit Workshop – $499

• Duration: 12 hours
• Ideal for: Students, entry-level cheminformatics professionals
• Modules Covered:
  • Module 1: Introduction to RDKit and Cheminformatics
  • Module 2: Molecular Representations and Structure Handling
  • Module 3: Molecular Fingerprints and Descriptor Calculation

2. Intermediate RDKit Training – $999

• Duration: 25 hours
• Ideal for: Industry professionals, bioinformatics researchers
• Modules Covered:
  • Module 1 to Module 3 (Basic Topics)
  • Module 4: Substructure Searching and Functional Group Identification
  • Module 5: Chemical Reactions and Molecular Transformations
  • Module 6: Machine Learning with RDKit – QSAR Modeling

3. Full RDKit Professional Course – $2,499

• Duration: 40+ hours
• Ideal for: Computational chemists, AI-driven drug discovery experts
• Modules Covered:
  • Module 1 to Module 6 (Basic & Intermediate Topics)
  • Module 7: Virtual Screening and Molecular Docking
  • Module 8: RDKit Integration with Data Science and Workflow Automation
  • Module 9: Customizing RDKit for Advanced Research

4. Custom Corporate Training – Starts at $5,000

• Duration: 50+ hours (Customized as per industry needs)
• Ideal for: Pharma R&D teams, biotech startups, cheminformatics research institutions
• Modules Covered:
  • All Modules (1-9) + Custom Modules Based on Enterprise Requirements
  • Module 10: Real-World Case Studies and Advanced Project Work